There are many difficulties in identifying complex events, such as recognizing a set of specific human behaviors combined with interactions with inanimate objects. At the same time, the amount of digital data being produced with meaningful event data is increasing exponentially. A single location today can have 6,000 video feeds and 3,000 microphones or highly sensitive air sensors. Timely detection of meaningful events can be helpful in a number of ways, including saving lives and resources.
Today's solutions are incremental additions to existing methodologies. The primary solution is to have a Network Operations Center (NOC), which is located physically near the appropriate sensor devices. Video feeds are typically viewed randomly by a 24/7 security staff. Some products can be installed to provide basic object detection, such as facial recognition, which in turn helps the security staff identify locations to investigate.
The field of object recognition has not advanced sufficiently to enable highly accurate detection of relevant data in real-time. Available systems come with the object detection systems built into the system, and today, there are only a few that are used for event detection. One issue that arises is that aspects of the object detection need to be defined prior to selling and deploying a product. Lack of confidence factors in object detection has led to an adversity in connecting multiple events, such as human behavior to specific usage of an identified object. Additionally, these events and behaviors may change rapidly over time, so that detection systems become quickly outdated. Because of the complexity in creating a framework around detecting objects, the best known generic system in widespread use is facial recognition. However, these systems have difficulty in processing images and providing highly accurate results in real-time. As new types of threats or actions are identified, these systems fall short of their ability to detect these new types of events. Due to the volume of real-time data, existing systems are used more in a forensics situation, rather than in attempting to provide real-time event data across most or all available feeds.
Clearly, it is important to develop technology that is adapted to detecting potential threats to the populace. However, this technology can be used in many other applications where historical data developed over time can be employed for detecting when abnormal behaviors or events are occurring in real-time data feeds. The applicability of such technology is virtually limitless and can range from detecting aberrant behavior of students in schools, where such behaviors might develop into a student harming others, to identifying potentially harmful drugs being sold on the streets.
As an alternative application of such technology, where real-time activities are observed, it would be desirable to be able to find all historically similar events, since in some cases, this information can be critical to many forensic and public safety situations. For example, knowing that a person of interest is meeting another individual, it would be desirable to be able to search historical archives for the individual. A review of such prior activities as captured in the historical archives might lead to developing information needed to understand the activities in which the individual is a participant, which are currently taking place. Yet, in many cases, finding information about an individual using currently available algorithms is impossible, for example, due to poor video quality and/or imperfect algorithms.